Deconvolution software? - "Er, what?" I hear you mutter. OK, here's what it might be able to do:

.......................Centre of a large cluster of galaxies observed with different spatial resolutions. When spatial resolution improves, finer details can be distinguished (as, in this image, the arcs formed by gravitational lensing).

The technique is used in microscopy and, as you can see from the image above, in astrophotography. There are a number of commercial and free packages out there which can apply deconvolution but the "MCS" algorithm used in that image isn't one of them, so far as I know.

Maybe one day one may be able to buy it as, from page 15 of this paper, they are getting to the point where a graphics processor can be used to accelerate processing of images 2048 pixels on a side in about an hour on a laptop. The market may be small but the benefits might be huge for those of us without access to full-on adaptive optics replete with high powered lasers and mountain-top observatories.

As a final example check out these deconvolutions of a simulated image of a star cluster partly superimposed on a background galaxy.

Yep, bottom left is what you might get from your camera and bottom right is MCS Deconvolution of that image, it's far and away the best in this simulated run as one can see by comparing it with the original image at top left and you can't buy it.

Aargh!

Any academics out there want to take on the challenge of making this algorithm more widely available? I really hope so.

Bob.

P.S. Although this is an "image processing" thread I think it belongs in this section because it is only of relevance to "Technical and scientific photography".

Edit: Upon revisiting this thread I found a number of links and images were no longer available so I've updated the post as well as I could.

I remember doing a bit of research into this a couple of months back when it was suggested on one of my images. I was told to search for Richardson-Lucy Deconvolution which was interesting, I think its the algorithm used in Maxim DL. However, I struggled to find any free packages that offer a good technique. The closest I got was a recommendation that Photoshop's Smart Sharpen was a form of deconvolution.

I must admit I havent really tried it out in anger yet, most of it goes way above my head, lol

I've seen the same about PhotoShop's Smart Sharpen tool - there's a YouTube video somewhere showing how much better Smart Sharpen can be compared to the Unsharp Mask tool.

One program which sells at a reasonable price is Astroart but I've yet to try it. Apparently it offers a choice of Richardson-Lucy, Van Cittert, Maximum Entropy or Wiener algorithms. Three of those (Van Cittert is missing) are shown in the simulation in my first post and all three introduce artefacts in the nebulosity.

The MCS algorithm is just so good, apparently, that the only reason I can think of for it not already being the standard is that it is too compute intensive but with GPU based (CUDA) acceleration that shouldn't be an issue in the future. Maybe in unskilled hands it can also produce undesirable results but it is already being used on some Hubble imagery and we amateurs can always calibrate our results against known high resolution images from the professionals. That would not only allow us to get the best possible images from our more modest equipment but might also allow us a better chance to produce good science.

I had a reply from Pierre today and the current state of play is that the MCS software only runs under Linux. He says there is no reason why it wouldn't run under Windows but nobody has written the code yet. One further observation is that, in its present version, it needs some training to be used efficiently. I didn't ask the question but my guess is that while the code might be shared with academics they might be unwilling to let it out to a wider audience until they can be sure that it won't be badly used and so give the algorithm a bad reputation so killing off any chance of a future commercial offering. Just my guess, though.